Monocular machine vision may offer limited capability in supporting obstacle detection for vehicles. An obstacle may be detected by identifying a monocular image feature that violates expected scene information. For example, monocular vision data may be used to potentially identify objects in a field that do not meet a soil image profile or a plant image profile as an obstacle. However, monocular vision data may be deficient in providing reliable identification of obstacles based on size, shape, depth, and other three dimensional geometric characteristics. Monocular machine vision tends to be over-inclusive or under-inclusive in obstacle identification because of the absence of three-dimensional data on the size and shape of a perceived obstacle. Further, the collection of monocular vision data is susceptible to distortion or inaccuracy arising from variations in illumination across a field of view. Although three-dimensional shape data of an object may be collected via a laser range finder that scans the surface of the object, the process may be too slow to be practical for vehicular navigation. Thus, there is need for an obstacle detection method and system that uses stereo vision to reliably identify obstacles.